[Python] Turning list of UserDict into numpy array

Because plain dict is not adequate for inheritance, I design the following MyDict with UserDict in python standard libraries:

import numpy as np
from collections import UserDict

class MyUserDict(UserDict):
pass

m_dict = {'a': np.arange(2),
'b': np.arange(2)}
m_mydict = MyUserDict(m_dict)


Then I meet a problem. When turning a list of UserDict into numpy array, values in UserDict have lost:

m_dict_array = np.array([m_dict, m_dict])
m_mydict_array = np.array([m_mydict, m_mydict])
print(m_dict_array)
print(m_mydict_array)

""" The output is
[{'a': array([0, 1]), 'b': array([0, 1])}
{'a': array([0, 1]), 'b': array([0, 1])}]
[['a' 'b']
['a' 'b']]
"""


The solution I find is adding __array__() method to MyUserDict:

class MyUserDict(UserDict):
def __array__(self):
# Prevent NumPy from converting to an array of keys
return np.array(self.data, dtype=object)

m_mydict = MyUserDict(m_dict)
m_mydict_array = np.array([m_mydict, m_mydict])
print(m_mydict_array)


This indeed solve my problem. But another problem I quickly realized is that self.data is plain dict and elements in m_mydict_array may be also plain self.data. So, I check the type as follows:

print(type(m_mydict_array[0]))


The type is MyUserDict I defined. This is certainly the result I hoped for, but I have no idea why it turned out like this.

Continue reading...